Overview

Dataset statistics

Number of variables19
Number of observations8636
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory152.0 B

Variable types

Numeric18
Categorical1

Alerts

cust_id has a high cardinality: 8636 distinct values High cardinality
balance is highly correlated with balance_frequency and 5 other fieldsHigh correlation
balance_frequency is highly correlated with balance and 1 other fieldsHigh correlation
purchases is highly correlated with oneoff_purchases and 5 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance is highly correlated with balance and 2 other fieldsHigh correlation
purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases and 2 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance_frequency is highly correlated with balance and 2 other fieldsHigh correlation
cash_advance_trx is highly correlated with balance and 2 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 5 other fieldsHigh correlation
minimum_payments is highly correlated with balance and 1 other fieldsHigh correlation
prc_full_payment is highly correlated with balanceHigh correlation
balance is highly correlated with credit_limitHigh correlation
purchases is highly correlated with oneoff_purchases and 3 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_frequency and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with oneoff_purchases_frequency and 2 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with oneoff_purchases and 2 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with installments_purchases and 2 other fieldsHigh correlation
cash_advance_frequency is highly correlated with cash_advance and 1 other fieldsHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 5 other fieldsHigh correlation
credit_limit is highly correlated with balanceHigh correlation
payments is highly correlated with purchases and 1 other fieldsHigh correlation
balance is highly correlated with minimum_paymentsHigh correlation
purchases is highly correlated with oneoff_purchases and 4 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 1 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_frequency and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases and 1 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with installments_purchases and 2 other fieldsHigh correlation
cash_advance_frequency is highly correlated with cash_advance and 1 other fieldsHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 3 other fieldsHigh correlation
minimum_payments is highly correlated with balanceHigh correlation
balance is highly correlated with credit_limitHigh correlation
purchases is highly correlated with oneoff_purchases and 4 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 1 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_trx and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with oneoff_purchases_frequency and 1 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases_frequencyHigh correlation
purchases_installments_frequency is highly correlated with purchases_frequencyHigh correlation
cash_advance_frequency is highly correlated with cash_advance_trxHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 3 other fieldsHigh correlation
credit_limit is highly correlated with balance and 2 other fieldsHigh correlation
payments is highly correlated with purchases and 4 other fieldsHigh correlation
df_index is uniformly distributed Uniform
cust_id is uniformly distributed Uniform
df_index has unique values Unique
cust_id has unique values Unique
payments has unique values Unique
purchases has 1967 (22.8%) zeros Zeros
oneoff_purchases has 4113 (47.6%) zeros Zeros
installments_purchases has 3747 (43.4%) zeros Zeros
cash_advance has 4431 (51.3%) zeros Zeros
purchases_frequency has 1966 (22.8%) zeros Zeros
oneoff_purchases_frequency has 4113 (47.6%) zeros Zeros
purchases_installments_frequency has 3746 (43.4%) zeros Zeros
cash_advance_frequency has 4431 (51.3%) zeros Zeros
cash_advance_trx has 4431 (51.3%) zeros Zeros
purchases_trx has 1967 (22.8%) zeros Zeros
prc_full_payment has 5589 (64.7%) zeros Zeros

Reproduction

Analysis started2022-02-23 23:00:03.242437
Analysis finished2022-02-23 23:01:10.457290
Duration1 minute and 7.21 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct8636
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4476.918828
Minimum0
Maximum8949
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:10.573477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile468.75
Q12266.75
median4468.5
Q36697.25
95-th percentile8489.25
Maximum8949
Range8949
Interquartile range (IQR)4430.5

Descriptive statistics

Standard deviation2565.759792
Coefficient of variation (CV)0.5731084012
Kurtosis-1.189516908
Mean4476.918828
Median Absolute Deviation (MAD)2215
Skewness0.003298459329
Sum38662671
Variance6583123.311
MonotonicityStrictly increasing
2022-02-23T20:01:10.861476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
59451
 
< 0.1%
59591
 
< 0.1%
59581
 
< 0.1%
59571
 
< 0.1%
59561
 
< 0.1%
59551
 
< 0.1%
59541
 
< 0.1%
59531
 
< 0.1%
59521
 
< 0.1%
Other values (8626)8626
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
89491
< 0.1%
89481
< 0.1%
89471
< 0.1%
89451
< 0.1%
89431
< 0.1%
89421
< 0.1%
89411
< 0.1%
89401
< 0.1%
89391
< 0.1%
89381
< 0.1%

cust_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct8636
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size67.6 KiB
C10001
 
1
C16111
 
1
C16125
 
1
C16124
 
1
C16123
 
1
Other values (8631)
8631 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8636 ?
Unique (%)100.0%

Sample

1st rowC10001
2nd rowC10002
3rd rowC10003
4th rowC10005
5th rowC10006

Common Values

ValueCountFrequency (%)
C100011
 
< 0.1%
C161111
 
< 0.1%
C161251
 
< 0.1%
C161241
 
< 0.1%
C161231
 
< 0.1%
C161221
 
< 0.1%
C161211
 
< 0.1%
C161201
 
< 0.1%
C161191
 
< 0.1%
C161181
 
< 0.1%
Other values (8626)8626
99.9%

Length

2022-02-23T20:01:11.072923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c100011
 
< 0.1%
c100201
 
< 0.1%
c100061
 
< 0.1%
c100071
 
< 0.1%
c100081
 
< 0.1%
c100091
 
< 0.1%
c100101
 
< 0.1%
c100111
 
< 0.1%
c100121
 
< 0.1%
c100131
 
< 0.1%
Other values (8626)8626
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8631
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1601.224893
Minimum0
Maximum19043.13856
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:11.305417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.209472
Q1148.095189
median916.855459
Q32105.195853
95-th percentile5936.635587
Maximum19043.13856
Range19043.13856
Interquartile range (IQR)1957.100664

Descriptive statistics

Standard deviation2095.5713
Coefficient of variation (CV)1.308730154
Kurtosis7.55387602
Mean1601.224893
Median Absolute Deviation (MAD)825.6064455
Skewness2.374254167
Sum13828178.17
Variance4391419.074
MonotonicityNot monotonic
2022-02-23T20:01:11.634689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
0.1%
40.9007491
 
< 0.1%
1253.1883171
 
< 0.1%
394.6435431
 
< 0.1%
617.4137261
 
< 0.1%
765.1095931
 
< 0.1%
2583.2478811
 
< 0.1%
1146.6693641
 
< 0.1%
757.4702011
 
< 0.1%
5058.2996351
 
< 0.1%
Other values (8621)8621
99.8%
ValueCountFrequency (%)
06
0.1%
0.0001991
 
< 0.1%
0.0011461
 
< 0.1%
0.0012141
 
< 0.1%
0.0012891
 
< 0.1%
0.0048161
 
< 0.1%
0.0096841
 
< 0.1%
0.0648111
 
< 0.1%
0.0654021
 
< 0.1%
0.0747241
 
< 0.1%
ValueCountFrequency (%)
19043.138561
< 0.1%
18495.558551
< 0.1%
16304.889251
< 0.1%
16259.448571
< 0.1%
16115.59641
< 0.1%
15532.339721
< 0.1%
15258.22591
< 0.1%
15244.748651
< 0.1%
15155.532861
< 0.1%
14581.459141
< 0.1%

balance_frequency
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8950351114
Minimum0
Maximum1
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:12.056806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.363636
Q10.909091
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.090909

Descriptive statistics

Standard deviation0.2076968758
Coefficient of variation (CV)0.2320544447
Kurtosis3.369586149
Mean0.8950351114
Median Absolute Deviation (MAD)0
Skewness-2.084161482
Sum7729.523222
Variance0.0431379922
MonotonicityNot monotonic
2022-02-23T20:01:12.269973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
16130
71.0%
0.909091406
 
4.7%
0.818182274
 
3.2%
0.727273220
 
2.5%
0.545455217
 
2.5%
0.636364202
 
2.3%
0.454545170
 
2.0%
0.363636167
 
1.9%
0.272727141
 
1.6%
0.181818117
 
1.4%
Other values (32)592
 
6.9%
ValueCountFrequency (%)
06
 
0.1%
0.09090925
 
0.3%
0.12
 
< 0.1%
0.1252
 
< 0.1%
0.1428571
 
< 0.1%
0.1666671
 
< 0.1%
0.181818117
1.4%
0.27
 
0.1%
0.2222222
 
< 0.1%
0.255
 
0.1%
ValueCountFrequency (%)
16130
71.0%
0.909091406
 
4.7%
0.955
 
0.6%
0.88888953
 
0.6%
0.87557
 
0.7%
0.85714350
 
0.6%
0.83333359
 
0.7%
0.818182274
 
3.2%
0.820
 
0.2%
0.77777821
 
0.2%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6015
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1025.433874
Minimum0
Maximum49039.57
Zeros1967
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:12.414623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143.3675
median375.405
Q31145.98
95-th percentile4060.0925
Maximum49039.57
Range49039.57
Interquartile range (IQR)1102.6125

Descriptive statistics

Standard deviation2167.107984
Coefficient of variation (CV)2.113357124
Kurtosis108.677684
Mean1025.433874
Median Absolute Deviation (MAD)375.405
Skewness8.055789007
Sum8855646.94
Variance4696357.014
MonotonicityNot monotonic
2022-02-23T20:01:12.619672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01967
 
22.8%
45.6525
 
0.3%
15015
 
0.2%
6013
 
0.2%
20012
 
0.1%
45012
 
0.1%
10012
 
0.1%
60010
 
0.1%
7010
 
0.1%
10009
 
0.1%
Other values (6005)6551
75.9%
ValueCountFrequency (%)
01967
22.8%
0.013
 
< 0.1%
0.051
 
< 0.1%
0.241
 
< 0.1%
12
 
< 0.1%
21
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
4.991
 
< 0.1%
6.91
 
< 0.1%
ValueCountFrequency (%)
49039.571
< 0.1%
41050.41
< 0.1%
40040.711
< 0.1%
38902.711
< 0.1%
35131.161
< 0.1%
32539.781
< 0.1%
31299.351
< 0.1%
27957.681
< 0.1%
27790.421
< 0.1%
26784.621
< 0.1%

oneoff_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3922
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean604.9014382
Minimum0
Maximum40761.25
Zeros4113
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:12.831603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44.995
Q3599.1
95-th percentile2728.3725
Maximum40761.25
Range40761.25
Interquartile range (IQR)599.1

Descriptive statistics

Standard deviation1684.307803
Coefficient of variation (CV)2.784433458
Kurtosis160.1213079
Mean604.9014382
Median Absolute Deviation (MAD)44.995
Skewness9.935775933
Sum5223928.82
Variance2836892.776
MonotonicityNot monotonic
2022-02-23T20:01:12.987466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04113
47.6%
45.6543
 
0.5%
5016
 
0.2%
20015
 
0.2%
7012
 
0.1%
15012
 
0.1%
100012
 
0.1%
10012
 
0.1%
25011
 
0.1%
6010
 
0.1%
Other values (3912)4380
50.7%
ValueCountFrequency (%)
04113
47.6%
0.016
 
0.1%
0.022
 
< 0.1%
0.051
 
< 0.1%
0.241
 
< 0.1%
14
 
< 0.1%
1.41
 
< 0.1%
21
 
< 0.1%
4.991
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
40761.251
< 0.1%
40624.061
< 0.1%
34087.731
< 0.1%
33803.841
< 0.1%
26547.431
< 0.1%
26514.321
< 0.1%
25122.771
< 0.1%
24543.521
< 0.1%
23032.971
< 0.1%
22257.391
< 0.1%

installments_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4341
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.8435329
Minimum0
Maximum22500
Zeros3747
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:13.169525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94.785
Q3484.1475
95-th percentile1800
Maximum22500
Range22500
Interquartile range (IQR)484.1475

Descriptive statistics

Standard deviation917.2451825
Coefficient of variation (CV)2.179539688
Kurtosis94.19337343
Mean420.8435329
Median Absolute Deviation (MAD)94.785
Skewness7.216133309
Sum3634404.75
Variance841338.7248
MonotonicityNot monotonic
2022-02-23T20:01:13.336076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03747
43.4%
10014
 
0.2%
20013
 
0.2%
12511
 
0.1%
15011
 
0.1%
30010
 
0.1%
759
 
0.1%
4508
 
0.1%
5008
 
0.1%
2707
 
0.1%
Other values (4331)4798
55.6%
ValueCountFrequency (%)
03747
43.4%
1.951
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
6.331
 
< 0.1%
7.261
 
< 0.1%
7.671
 
< 0.1%
9.581
 
< 0.1%
9.651
 
< 0.1%
9.681
 
< 0.1%
ValueCountFrequency (%)
225001
< 0.1%
15497.191
< 0.1%
14686.11
< 0.1%
13184.431
< 0.1%
12738.471
< 0.1%
12560.851
< 0.1%
125411
< 0.1%
123751
< 0.1%
12235.051
< 0.1%
12128.941
< 0.1%

cash_advance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4206
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean994.175523
Minimum0
Maximum47137.21176
Zeros4431
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:13.485864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31132.38549
95-th percentile4721.415498
Maximum47137.21176
Range47137.21176
Interquartile range (IQR)1132.38549

Descriptive statistics

Standard deviation2121.458303
Coefficient of variation (CV)2.133887079
Kurtosis52.14352309
Mean994.175523
Median Absolute Deviation (MAD)0
Skewness5.139628566
Sum8585699.817
Variance4500585.331
MonotonicityNot monotonic
2022-02-23T20:01:13.620306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04431
51.3%
2411.5842481
 
< 0.1%
92.65791
 
< 0.1%
1486.2432931
 
< 0.1%
855.2327791
 
< 0.1%
3767.1047071
 
< 0.1%
291.6085121
 
< 0.1%
38.6905521
 
< 0.1%
521.6643691
 
< 0.1%
1974.2029631
 
< 0.1%
Other values (4196)4196
48.6%
ValueCountFrequency (%)
04431
51.3%
14.2222161
 
< 0.1%
18.0427681
 
< 0.1%
18.1179671
 
< 0.1%
18.1234131
 
< 0.1%
18.1266831
 
< 0.1%
18.1499461
 
< 0.1%
18.2045771
 
< 0.1%
18.2406261
 
< 0.1%
18.2800431
 
< 0.1%
ValueCountFrequency (%)
47137.211761
< 0.1%
29282.109151
< 0.1%
27296.485761
< 0.1%
26268.699891
< 0.1%
26194.049541
< 0.1%
23130.821061
< 0.1%
22665.77851
< 0.1%
21943.849421
< 0.1%
20712.670081
< 0.1%
20277.331121
< 0.1%

purchases_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4960000002
Minimum0
Maximum1
Zeros1966
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:13.770020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.4012726404
Coefficient of variation (CV)0.8090174198
Kurtosis-1.638001348
Mean0.4960000002
Median Absolute Deviation (MAD)0.416667
Skewness0.03304121629
Sum4283.456002
Variance0.161019732
MonotonicityNot monotonic
2022-02-23T20:01:13.936581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
12126
24.6%
01966
22.8%
0.083333622
 
7.2%
0.916667391
 
4.5%
0.5390
 
4.5%
0.833333367
 
4.2%
0.166667367
 
4.2%
0.333333350
 
4.1%
0.25328
 
3.8%
0.583333309
 
3.6%
Other values (37)1420
16.4%
ValueCountFrequency (%)
01966
22.8%
0.083333622
 
7.2%
0.09090941
 
0.5%
0.123
 
0.3%
0.11111116
 
0.2%
0.12525
 
0.3%
0.14285722
 
0.3%
0.166667367
 
4.2%
0.18181815
 
0.2%
0.217
 
0.2%
ValueCountFrequency (%)
12126
24.6%
0.916667391
 
4.5%
0.90909128
 
0.3%
0.923
 
0.3%
0.88888918
 
0.2%
0.87526
 
0.3%
0.85714323
 
0.3%
0.833333367
 
4.2%
0.81818220
 
0.2%
0.89
 
0.1%

oneoff_purchases_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2059087409
Minimum0
Maximum1
Zeros4113
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:14.102077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.333333
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.333333

Descriptive statistics

Standard deviation0.3000536066
Coefficient of variation (CV)1.457216461
Kurtosis1.05820574
Mean0.2059087409
Median Absolute Deviation (MAD)0.083333
Skewness1.504234233
Sum1778.227886
Variance0.09003216681
MonotonicityNot monotonic
2022-02-23T20:01:14.269654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
04113
47.6%
0.0833331057
 
12.2%
0.166667576
 
6.7%
1469
 
5.4%
0.25408
 
4.7%
0.333333346
 
4.0%
0.416667243
 
2.8%
0.5232
 
2.7%
0.583333197
 
2.3%
0.666667167
 
1.9%
Other values (37)828
 
9.6%
ValueCountFrequency (%)
04113
47.6%
0.0833331057
 
12.2%
0.09090954
 
0.6%
0.136
 
0.4%
0.11111124
 
0.3%
0.12535
 
0.4%
0.14285733
 
0.4%
0.166667576
 
6.7%
0.18181833
 
0.4%
0.226
 
0.3%
ValueCountFrequency (%)
1469
5.4%
0.916667151
 
1.7%
0.9090914
 
< 0.1%
0.91
 
< 0.1%
0.8888892
 
< 0.1%
0.8756
 
0.1%
0.8571431
 
< 0.1%
0.833333115
 
1.3%
0.81818210
 
0.1%
0.84
 
< 0.1%

purchases_installments_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3688203456
Minimum0
Maximum1
Zeros3746
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:14.518620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.3980929442
Coefficient of variation (CV)1.079368177
Kurtosis-1.4192794
Mean0.3688203456
Median Absolute Deviation (MAD)0.166667
Skewness0.4877529543
Sum3185.132505
Variance0.1584779922
MonotonicityNot monotonic
2022-02-23T20:01:14.752564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
03746
43.4%
11297
 
15.0%
0.416667381
 
4.4%
0.916667340
 
3.9%
0.833333304
 
3.5%
0.5303
 
3.5%
0.166667296
 
3.4%
0.666667290
 
3.4%
0.75284
 
3.3%
0.083333249
 
2.9%
Other values (37)1146
 
13.3%
ValueCountFrequency (%)
03746
43.4%
0.083333249
 
2.9%
0.09090910
 
0.1%
0.15
 
0.1%
0.1111118
 
0.1%
0.1254
 
< 0.1%
0.1428575
 
0.1%
0.166667296
 
3.4%
0.18181814
 
0.2%
0.28
 
0.1%
ValueCountFrequency (%)
11297
15.0%
0.916667340
 
3.9%
0.90909125
 
0.3%
0.918
 
0.2%
0.88888928
 
0.3%
0.87528
 
0.3%
0.85714329
 
0.3%
0.833333304
 
3.5%
0.81818220
 
0.2%
0.817
 
0.2%

cash_advance_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1376042043
Minimum0
Maximum1.5
Zeros4431
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:14.919040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.2017914306
Coefficient of variation (CV)1.466462683
Kurtosis3.184233316
Mean0.1376042043
Median Absolute Deviation (MAD)0
Skewness1.795915039
Sum1188.349908
Variance0.04071978147
MonotonicityNot monotonic
2022-02-23T20:01:15.119644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04431
51.3%
0.083333980
 
11.3%
0.166667730
 
8.5%
0.25573
 
6.6%
0.333333434
 
5.0%
0.416667272
 
3.1%
0.5209
 
2.4%
0.583333142
 
1.6%
0.666667124
 
1.4%
0.09090966
 
0.8%
Other values (44)675
 
7.8%
ValueCountFrequency (%)
04431
51.3%
0.083333980
 
11.3%
0.09090966
 
0.8%
0.136
 
0.4%
0.11111123
 
0.3%
0.12543
 
0.5%
0.14285743
 
0.5%
0.166667730
 
8.5%
0.18181841
 
0.5%
0.221
 
0.2%
ValueCountFrequency (%)
1.51
 
< 0.1%
1.251
 
< 0.1%
1.1666672
 
< 0.1%
1.1428571
 
< 0.1%
1.1251
 
< 0.1%
1.11
 
< 0.1%
1.0909091
 
< 0.1%
124
0.3%
0.91666727
0.3%
0.9090913
 
< 0.1%

cash_advance_trx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.313918481
Minimum0
Maximum123
Zeros4431
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:15.461817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.912506117
Coefficient of variation (CV)2.085901074
Kurtosis60.42852324
Mean3.313918481
Median Absolute Deviation (MAD)0
Skewness5.67332683
Sum28619
Variance47.78274082
MonotonicityNot monotonic
2022-02-23T20:01:15.685840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04431
51.3%
1839
 
9.7%
2602
 
7.0%
3429
 
5.0%
4374
 
4.3%
5300
 
3.5%
6241
 
2.8%
7202
 
2.3%
8169
 
2.0%
10147
 
1.7%
Other values (55)902
 
10.4%
ValueCountFrequency (%)
04431
51.3%
1839
 
9.7%
2602
 
7.0%
3429
 
5.0%
4374
 
4.3%
5300
 
3.5%
6241
 
2.8%
7202
 
2.3%
8169
 
2.0%
9108
 
1.3%
ValueCountFrequency (%)
1233
< 0.1%
1101
 
< 0.1%
1071
 
< 0.1%
931
 
< 0.1%
801
 
< 0.1%
711
 
< 0.1%
691
 
< 0.1%
631
 
< 0.1%
623
< 0.1%
611
 
< 0.1%

purchases_trx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.03323298
Minimum0
Maximum358
Zeros1967
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:15.879577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q318
95-th percentile59
Maximum358
Range358
Interquartile range (IQR)17

Descriptive statistics

Standard deviation25.1804684
Coefficient of variation (CV)1.674986906
Kurtosis33.95227887
Mean15.03323298
Median Absolute Deviation (MAD)7
Skewness4.578418451
Sum129827
Variance634.0559887
MonotonicityNot monotonic
2022-02-23T20:01:16.083828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01967
22.8%
1606
 
7.0%
12537
 
6.2%
2345
 
4.0%
6340
 
3.9%
3294
 
3.4%
4277
 
3.2%
7265
 
3.1%
8263
 
3.0%
5254
 
2.9%
Other values (163)3488
40.4%
ValueCountFrequency (%)
01967
22.8%
1606
 
7.0%
2345
 
4.0%
3294
 
3.4%
4277
 
3.2%
5254
 
2.9%
6340
 
3.9%
7265
 
3.1%
8263
 
3.0%
9240
 
2.8%
ValueCountFrequency (%)
3581
< 0.1%
3471
< 0.1%
3441
< 0.1%
3091
< 0.1%
3081
< 0.1%
2981
< 0.1%
2741
< 0.1%
2731
< 0.1%
2541
< 0.1%
2482
< 0.1%

credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct203
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4522.09103
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:16.251535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3659.240379
Coefficient of variation (CV)0.8091921094
Kurtosis2.773473055
Mean4522.09103
Median Absolute Deviation (MAD)1800
Skewness1.507019041
Sum39052778.13
Variance13390040.15
MonotonicityNot monotonic
2022-02-23T20:01:16.434450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000752
 
8.7%
1500695
 
8.0%
1200597
 
6.9%
1000596
 
6.9%
2500584
 
6.8%
4000471
 
5.5%
6000449
 
5.2%
5000370
 
4.3%
2000364
 
4.2%
7500273
 
3.2%
Other values (193)3485
40.4%
ValueCountFrequency (%)
501
 
< 0.1%
1505
 
0.1%
2003
 
< 0.1%
30014
 
0.2%
4003
 
< 0.1%
4506
 
0.1%
500112
1.3%
60021
 
0.2%
6501
 
< 0.1%
70020
 
0.2%
ValueCountFrequency (%)
300002
 
< 0.1%
280001
 
< 0.1%
250001
 
< 0.1%
230002
 
< 0.1%
225001
 
< 0.1%
220001
 
< 0.1%
215002
 
< 0.1%
210002
 
< 0.1%
205001
 
< 0.1%
2000010
0.1%

payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8636
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1784.478099
Minimum0.049513
Maximum50721.48336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:16.583251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.049513
5-th percentile143.5595672
Q1418.5592373
median896.675701
Q31951.14209
95-th percentile6152.318659
Maximum50721.48336
Range50721.43385
Interquartile range (IQR)1532.582853

Descriptive statistics

Standard deviation2909.81009
Coefficient of variation (CV)1.630622473
Kurtosis54.27081446
Mean1784.478099
Median Absolute Deviation (MAD)592.6225195
Skewness5.873048587
Sum15410752.86
Variance8466994.762
MonotonicityNot monotonic
2022-02-23T20:01:16.735359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201.8020841
 
< 0.1%
6372.6190371
 
< 0.1%
162.9492361
 
< 0.1%
164.4037391
 
< 0.1%
1679.004861
 
< 0.1%
209.3927291
 
< 0.1%
1014.5496331
 
< 0.1%
272.5177481
 
< 0.1%
32.9243841
 
< 0.1%
1899.7382861
 
< 0.1%
Other values (8626)8626
99.9%
ValueCountFrequency (%)
0.0495131
< 0.1%
0.0564661
< 0.1%
3.5005051
< 0.1%
4.5235551
< 0.1%
4.8415431
< 0.1%
9.5333131
< 0.1%
12.7731441
< 0.1%
14.5006881
< 0.1%
16.3854211
< 0.1%
18.1255271
< 0.1%
ValueCountFrequency (%)
50721.483361
< 0.1%
46930.598241
< 0.1%
40627.595241
< 0.1%
39461.96581
< 0.1%
39048.597621
< 0.1%
36066.750681
< 0.1%
35843.625931
< 0.1%
34107.074991
< 0.1%
33994.727851
< 0.1%
33486.310441
< 0.1%

minimum_payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct8635
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean864.3049429
Minimum0.019163
Maximum76406.20752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:16.898873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.019163
5-th percentile73.3585415
Q1169.1635455
median312.4522915
Q3825.4964627
95-th percentile2766.593894
Maximum76406.20752
Range76406.18836
Interquartile range (IQR)656.3329173

Descriptive statistics

Standard deviation2372.56635
Coefficient of variation (CV)2.745057019
Kurtosis283.9630416
Mean864.3049429
Median Absolute Deviation (MAD)190.372786
Skewness13.62219309
Sum7464137.487
Variance5629071.086
MonotonicityNot monotonic
2022-02-23T20:01:17.052670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299.3518812
 
< 0.1%
342.286491
 
< 0.1%
184.4647211
 
< 0.1%
276.4860721
 
< 0.1%
309.1408651
 
< 0.1%
354.2811141
 
< 0.1%
216.0904331
 
< 0.1%
277.5467131
 
< 0.1%
150.3171431
 
< 0.1%
1600.269171
 
< 0.1%
Other values (8625)8625
99.9%
ValueCountFrequency (%)
0.0191631
< 0.1%
0.0377441
< 0.1%
0.055881
< 0.1%
0.0594811
< 0.1%
0.1170361
< 0.1%
0.2619841
< 0.1%
0.3119531
< 0.1%
0.3194751
< 0.1%
1.1130271
< 0.1%
1.3340751
< 0.1%
ValueCountFrequency (%)
76406.207521
< 0.1%
61031.61861
< 0.1%
56370.041171
< 0.1%
50260.759471
< 0.1%
43132.728231
< 0.1%
42629.551171
< 0.1%
38512.124771
< 0.1%
31871.363791
< 0.1%
30528.43241
< 0.1%
29019.802881
< 0.1%

prc_full_payment
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1593036248
Minimum0
Maximum1
Zeros5589
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:17.221848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.166667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.166667

Descriptive statistics

Standard deviation0.2962709094
Coefficient of variation (CV)1.859787621
Kurtosis2.201598481
Mean0.1593036248
Median Absolute Deviation (MAD)0
Skewness1.88602713
Sum1375.746104
Variance0.08777645176
MonotonicityNot monotonic
2022-02-23T20:01:17.366230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
05589
64.7%
1488
 
5.7%
0.083333426
 
4.9%
0.166667166
 
1.9%
0.5156
 
1.8%
0.25156
 
1.8%
0.090909153
 
1.8%
0.333333134
 
1.6%
0.194
 
1.1%
0.283
 
1.0%
Other values (37)1191
 
13.8%
ValueCountFrequency (%)
05589
64.7%
0.083333426
 
4.9%
0.090909153
 
1.8%
0.194
 
1.1%
0.11111161
 
0.7%
0.12552
 
0.6%
0.14285754
 
0.6%
0.166667166
 
1.9%
0.18181875
 
0.9%
0.283
 
1.0%
ValueCountFrequency (%)
1488
5.7%
0.91666777
 
0.9%
0.90909119
 
0.2%
0.916
 
0.2%
0.88888912
 
0.1%
0.87518
 
0.2%
0.85714312
 
0.1%
0.83333363
 
0.7%
0.81818217
 
0.2%
0.833
 
0.4%

tenure
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.53439092
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-02-23T20:01:17.500264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.310983661
Coefficient of variation (CV)0.1136586812
Kurtosis8.15670142
Mean11.53439092
Median Absolute Deviation (MAD)0
Skewness-3.011140523
Sum99611
Variance1.718678158
MonotonicityNot monotonic
2022-02-23T20:01:17.599204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
127346
85.1%
11356
 
4.1%
10226
 
2.6%
6184
 
2.1%
8183
 
2.1%
7177
 
2.0%
9164
 
1.9%
ValueCountFrequency (%)
6184
 
2.1%
7177
 
2.0%
8183
 
2.1%
9164
 
1.9%
10226
 
2.6%
11356
 
4.1%
127346
85.1%
ValueCountFrequency (%)
127346
85.1%
11356
 
4.1%
10226
 
2.6%
9164
 
1.9%
8183
 
2.1%
7177
 
2.0%
6184
 
2.1%

Interactions

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Correlations

2022-02-23T20:01:17.733401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-23T20:01:18.036936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-23T20:01:18.340984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-23T20:01:18.615423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-23T20:01:09.889945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-23T20:01:10.297014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenure
00C1000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.00021000.0201.802084139.5097870.00000012
11C100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.25407000.04103.0325971072.3402170.22222212
22C100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.000127500.0622.066742627.2847870.00000012
34C10005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.00011200.0678.334763244.7912370.00000012
45C100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.00081800.01400.0577702407.2460350.00000012
56C10007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.0006413500.06354.314328198.0658941.00000012
67C100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.000122300.0679.065082532.0339900.00000012
78C100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.00057000.0688.278568311.9634090.00000012
89C10010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.000311000.01164.770591100.3022620.00000012
910C100111293.1249391.000000920.120.00920.120.0000001.0000000.0000001.0000000.000121200.01083.3010072172.6977650.00000012

Last rows

df_indexcust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenure
86268938C1917978.8184070.5000000.000.000.001113.1860780.0000000.0000000.0000000.166667701200.01397.77013121.8211940.3333336
86278939C19180728.3525481.000000734.40734.400.00239.8910380.3333330.3333330.0000000.166667221000.072.530037110.9507980.0000006
86288940C19181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.0000006
86298941C191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.0000006
86308942C1918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.2500006
86318943C191845.8717120.50000020.9020.900.000.0000000.1666670.1666670.0000000.00000001500.058.64488343.4737170.0000006
86328945C1918628.4935171.000000291.120.00291.120.0000001.0000000.0000000.8333330.000000061000.0325.59446248.8863650.5000006
86338947C1918823.3986730.833333144.400.00144.400.0000000.8333330.0000000.6666670.000000051000.081.27077582.4183690.2500006
86348948C1918913.4575640.8333330.000.000.0036.5587780.0000000.0000000.0000000.16666720500.052.54995955.7556280.2500006
86358949C19190372.7080750.6666671093.251093.250.00127.0400080.6666670.6666670.0000000.3333332231200.063.16540488.2889560.0000006